Describes a Monte Carlo investigation of four methods for treating incomplete data: (1) resemblance based hot-deck imputation (RBHDI); (2) iterated stochastic regression imputation; (3) structured model expectation maximization; and (4) saturated model expectation maximization. Results favored the expectation maximization methods. (SLD)

Studied Type I error rates and relative power of structural means, multiple-indicator, multiple-cause, and multivariate analysis of variance approaches for testing construct mean differences within a one-factor, two-group design. Used Monte Carlo methods to investigate Type I error rates and a population analysis approach to study the power of…

Studied planned incomplete data designs for the purpose of substantially reducing the amount of data required for multitrait-multimethod models. Simulations studied the effectiveness of Listwise Deletion, Pairwise Deletion, and the expectation maximization (EM) algorithm. Results indicate that EM is generally precise and efficient. (SLD)

Examined the effects of ignoring multilevel data structures in nonhierarchical covariance modeling using a Monte Carlo simulation. Results suggest that when the magnitudes of intraclass correlations are less than 0.05 and the group size is small, the consequences of ignoring the data dependence within the multilevel data structures seem to be…

A Monte Carlo study is reported that shows the comparative performance of alternative approaches under deviations from their respective assumptions in the case of structural equation models with latent variables with attention restricted to point estimates of model parameters. The conditional polychoric correlations method is shown most robust…

Illustrates the design and planning of Monte Carlo simulations, presenting nine steps in planning and performing a Monte Carlo analysis from developing a theoretically derived question of interest through summarizing the results. Uses a Monte Carlo simulation to illustrate many of the relevant points. (SLD)

Demonstrates how substantive researchers can use a Monte Carlo study to decide on sample size and determine power. Presents confirmatory factor analysis and growth models as examples, conducting these analyses with the Mplus program (B. Muthen and L. Muthen 1998). (SLD)

Illustrates that sampling variance can be very large when a three-wave quasi simplex model is used to obtain reliability estimates. Also shows that, for the reliability parameter to be identified, the model assumes a Markov process. These problems are evaluated with both real and Monte Carlo data. (SLD)

Evaluated the performance of several alternative cluster analytic approaches to initial model specification using population parameter analyses and a Monte Carlo simulation. Of the six cluster approaches evaluated, the one using the correlations of item correlations as a proximity metric and average linking as a clustering algorithm performed the…

Used Monte Carlo simulation to examine the performance of four missing data methods in structural equation models: (1)full information maximum likelihood (FIML); (2) listwise deletion; (3) pairwise deletion; and (4) similar response pattern imputation. Results show that FIML estimation is superior across all conditions of the design. (SLD)

The behavior of eight measures of fit used to evaluate confirmatory factor analysis models was studied through Monte Carlo simulation to determine the extent to which sample size, model size, estimation procedure, and level of nonnormality affect fit when analyzing polytomous data. Implications of results for evaluating fit are discussed. (SLD)

Proposes the use of nonnested tests for the two stage least squares (2SLS) estimator of latent variable models to discriminate between scales. Compares the finite sample performance of these tests to structural equation modeling information-based criteria. Presents practical recommendations based on the Monte Carlo analysis. (SLD)

Studied the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data through Monte Carlo simulation. Proposes a model selection procedure for obtaining good models for the underlying substantive theory and discusses the effectiveness of the proposed model. (SLD)

In recent years, confirmatory factor analytic (CFA) techniques have become the most common method of testing for measurement equivalence/invariance (ME/I). However, no study has simulated data with known differences to determine how well these CFA techniques perform. This study utilizes data with a variety of known simulated differences in factor…

A Monte Carlo study evaluated the effectiveness of different factor analysis extraction and rotation methods for identifying the known population multiple-indicator measurement model. Results demonstrate that exploratory factor analysis can contribute to a useful heuristic strategy for model specification prior to cross-validation with…